LEE- Uçak ve Uzay Mühendisliği Lisansüstü Programı

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http://hdl.handle.net/11527/19473

Gözat

Konu "Aerodinamik" ile LEE- Uçak ve Uzay Mühendisliği Lisansüstü Programı'a göz atma
  • Öge
    Investigation of Reynolds number effects on aerodynamic characteristics of generic aircraft and estimation of Reynolds-dependent aerodynamic database using artificial neural network models
    (Graduate School, 2023-06-16) Karaaslan, Ramazan ; Özkol, İbrahim ; 511201140 ; Aeronautics and Astronautics Engineering
    This paper examines an Artificial Neural Network (ANN) model that was constructed to create a comprehensive aerodynamic database, including the effects of Reynolds number on the aerodynamic features of a generic aircraft model, with a particular emphasis on the impacts of altitude and scale. Using Computational Fluid Dynamics (CFD) simulations and an ANN model, a comprehensive aerodynamic database that accounts for the wide-ranging impacts of Reynolds number is generated. Using a generic aircraft model, a comprehensive set of CFD calculations were performed to evaluate the effect of Reynolds number on key aerodynamic properties. The simulations included different Mach numbers (0.2 to 0.95), angles of attack (AoA) ranging from -12 to 40 degrees, sideslip angles (Beta) ranging from 0 to 20 degrees, different altitudes, and different scales. The purpose of these simulations was to investigate the effect of Reynolds number on a variety of aerodynamic parameters, including as shock location translation, flow separation point, control surface efficiency, wing stall region change, and drag exchange. By carefully scrutinizing the CFD results, the effects of altitude and scale on the aerodynamic database were uncovered. The findings demonstrated that the Reynolds number, which was affected by both altitude and scale, significantly affected the aerodynamic behavior of the aircraft. It has been noted that the translation of shock location, flow separation point, wing stall region, and drag change are all sensitive to variations in Reynolds number. Moreover, the Reynolds number was found to influence the effectiveness of control surfaces such as flaps, ailerons, and rudder, which could have significant ramifications for the aircraft's overall performance, stability, and maneuverability. Using a subset of CFD results, an ANN model was created in order to construct a comprehensive aerodynamic database that accounts for Reynolds number effects. The ANN model displayed encouraging results in forecasting various aerodynamic characteristics based on Reynolds number, giving a valuable tool for comprehending and enhancing the performance of an aircraft under varying flying situations. This unique method enabled the effective identification of correlations between Reynolds number and aerodynamic properties, so contributing to a deeper comprehension of aircraft performance. This study illuminates the major influence of Reynolds number on the aerodynamic performance of a generic airplane model. Using CFD analyses and an ANN model, a comprehensive aerodynamic database accounting for the impacts of altitude, scale, and control surface efficiency was developed. This study's findings can be utilized to enhance the design and performance of aircraft, particularly in respect to the influence of Reynolds number on various aerodynamic parameters. Future study directions could include expanding the database to encompass a broader range of flight situations, examining the impacts of Reynolds number in supersonic and hypersonic flight regimes, and improving control surfaces further for improved efficiency and performance. In addition, the creation of new ANN models for certain flight zones and the incorporation of control surface-deflected CFD findings may provide more insights into the design and optimization of control surfaces. This study contributes to the growing body of knowledge regarding the critical function of Reynolds number in determining aerodynamic performance, with possible implications in the design and development of sophisticated aircraft and the broader area of aerodynamics.
  • Öge
    Quantitative analysis of aircraft aerodynamic derivatives using the least squares method in a six degrees of freedom flight simulation environment
    (Graduate School, 2024-08-21) Altınışık, Furkan ; Acar, Hayri ; 511201165 ; Aeronautical and Astronautical Engineering
    This thesis presents an in-depth analysis of aircraft aerodynamic derivatives using the Least Squares Method (LSM) within a six degrees of freedom (6-DOF) flight simulation environment. The primary objective is to evaluate and compare the performance of Ordinary Least Squares (OLS) and Recursive Least Squares (RLS) methods in estimating aerodynamic parameters under various flight conditions, including ideal, turbulent, and error-induced scenarios. A detailed 6-DOF flight simulation model was developed using data from the SIAI Marchetti S211 aircraft. This model integrates various subsystems, including equations of motion, aerodynamics, engine dynamics, and atmospheric conditions. The Newton-Raphson method was employed to maintain steady-state conditions, ensuring the aircraft's trim state was accurately represented. For solving the differential equations derived from the equations of motion, the Runge-Kutta method was chosen due to its robustness and accuracy in handling the nonlinearities associated with flight dynamics in the simulation model. The aerodynamic forces and moments were linearized using the small disturbance theorem, which simplifies the complex nonlinear equations into a more manageable linear form. This linearization allowed for the formulation of force and moment coefficients as functions of aerodynamic derivatives. These derivatives, critical for understanding the aircraft's behavior, were estimated using both OLS and RLS methods. Realistic flight data was simulated under various conditions, including ideal scenarios without any disturbances, scenarios with atmospheric turbulence, and scenarios with systematic sensor errors. The Dryden turbulence model was used to simulate realistic atmospheric disturbances, providing a continuous representation of turbulence that affects the aircraft during flight. Systematic sensor errors were introduced to understand their impact on the accuracy of parameter estimation. The OLS method provided single-step parameter estimates by processing all data points simultaneously, making it straightforward and computationally efficient. In contrast, the RLS method updated parameter estimates incrementally as new data became available. This dynamic approach allowed the RLS method to adapt to changes over time, making it particularly suitable for real-time applications where system characteristics may vary. Performance metrics such as the $R^2$ statistic and standard deviation were used to evaluate the estimation accuracy. These metrics provided quantitative measures of how well the estimated parameters matched the true values, with the $R^2$ statistic indicating the proportion of variance explained by the model and the standard deviation providing a measure of the estimation precision. The analysis revealed that both OLS and RLS methods produced accurate results under ideal and turbulent conditions. The presence of atmospheric turbulence did not significantly affect the estimation accuracy, as the average error introduced by the turbulence was zero. This robustness highlights the effectiveness of LSM in handling real-world flight data with environmental disturbances. However, when systematic sensor errors were introduced, both OLS and RLS methods showed biased estimation results. The bias was evident in the deviation of the estimated aerodynamic derivatives from their true values, underscoring the importance of accurate and error-free measurement data for reliable parameter estimation. Further analysis demonstrated that increasing the sampling frequency improved the performance of the RLS method. At higher frequencies, such as 50 kHz, the RLS estimates converged more closely to the true values, even in the presence of systematic sensor errors. This improvement is attributed to the reduced information loss in higher frequency sampling, which captures more details and variations in the data that might be missed at lower frequencies. This finding suggests that higher sampling rates can effectively mitigate the adverse effects of sensor errors on parameter estimation. The design of control surface inputs was identified as a crucial factor influencing the accuracy of aerodynamic parameter estimation. Optimal input design, which involves selecting appropriate control surface deflections, ensured accurate estimation results. Conversely, non-optimal inputs led to discrepancies between the estimated and true values. This emphasizes the need for carefully designed excitation maneuvers during flight tests to obtain reliable aerodynamic data. The RLS method demonstrated particular advantages in dynamic environments due to its ability to update estimates in real-time. This adaptive capability allowed it to maintain accuracy even when the system characteristics changed over time. However, the OLS method exhibited slightly better performance at lower frequencies, showing less sensitivity to variations in sampling rates. Both methods showed distinct strengths, with OLS excelling in stable, low-frequency scenarios and RLS proving superior in dynamic, high-frequency conditions. The theoretical expected value formulas for the parameter estimates were validated using the simulation model outputs. This validation confirmed the presence of bias when systematic errors were introduced and reinforced the high accuracy of estimates under both ideal and turbulent conditions. In conclusion, this thesis provides a comprehensive evaluation of OLS and RLS methods for estimating aerodynamic derivatives in a 6-DOF flight simulation environment. The findings demonstrate the robustness of these methods under various flight conditions, highlight the impact of systematic sensor errors, and underscore the importance of optimal input design and high-frequency data sampling under linear database.